






"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							None:
  lowerCamelCase_	,      lowerCamelCase_       	=		analyze_text(_lowerCamelCase					)
  lowerCamelCase_       	=		list(' ' + ascii_lowercase					)
  # what is our total sum of probabilities.
  lowerCamelCase_       	=		sum(single_char_strings.values()					)

  # one length string
  lowerCamelCase_       	=		0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowerCamelCase_       	=		single_char_strings[ch]
      lowerCamelCase_       	=		my_str / all_sum
      my_fir_sum += prob * math.loga(_lowerCamelCase					)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum					):.1f}'''					)

  # two len string
  lowerCamelCase_       	=		sum(two_char_strings.values()					)
  lowerCamelCase_       	=		0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowerCamelCase_       	=		cha + cha
      if sequence in two_char_strings:
        lowerCamelCase_       	=		two_char_strings[sequence]
        lowerCamelCase_       	=		int(_lowerCamelCase					) / all_sum
        my_sec_sum += prob * math.loga(_lowerCamelCase					)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum					):.1f}'''					)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'''					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							tuple[dict, dict]:
  lowerCamelCase_       	=		Counter()  # type: ignore
  lowerCamelCase_       	=		Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0						,							len(_lowerCamelCase					) - 1					):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings







def 	lowerCamelCase__			(				) ->							Tuple:
  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
  main()












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




